Quantifying constraints determining independent activation on NMDA receptors mediated currents from evoked and spontaneous synaptic transmission at an individual synapse

A synapse acts on neural transmission through a chemical process called synapses fusion between pre-synaptic and post-synaptic terminals. Presynaptic terminals release neurotransmitters either in response to action potential or spontaneously independent of presynaptic activity. However, it is still unclear the mechanism of evoked and spontaneous neuro-transmission that activate on postsynaptic terminals. To address this question, we examined the possibility that spontaneous and evoked neurotransmissions using mathematical simulations. We aimed to address the biophysical constraints that may determine independent activation on N-methyl-D-asparate (NMDA) receptor mediated currents in response to evoked and spontaneous glutamate molecules releases. In order to identify the spatial relation between spontaneous and evoked glutamate release, we considered quantitative factors, such as size of synapses, inhomogeneity of diffusion mobility, geometry of synaptic cleft, and release rate of neurotransmitter. Simulation results showed that as a synaptic size is smaller and if the cleft space is more cohesive in the peripheral area than the centre area, then there is high possibility of having crosstalk of two signals released from center and edge. When a synaptic size is larger, the cleft space is more affinity in the central area than the external area, and if the geometry of fusion has a narrower space, then those produce more chances of independence of two modes of currents released from center and edge. The computed results match well with existing experimental findings and serve as a road map for further exploration to identify independence of evoked and spontaneous releases

DAppSCAN: Building Large-Scale Datasets for Smart Contract Weaknesses in DApp Projects

The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these tools has proven challenging due to the absence of a large, unbiased, real-world dataset. To address this problem, we aim to build a large-scale SWC weakness dataset from real-world DApp projects. We recruited 22 participants and spent 44 person-months analyzing 1,199 open source audit reports from 29 security teams. In total, we identified 9,154 weaknesses and developed two distinct datasets, i.e., DAPPSCAN-SOURCE and DAPPSCAN-BYTECODE. The DAPPSCAN-SOURCE dataset comprises 39,904 Solidity files, featuring 1,618 SWC weaknesses sourced from 682 real-world DApp projects. However, the Solidity files in this dataset may not be directly compilable for further analysis. To facilitate automated analysis, we developed a tool capable of automatically identifying dependency relationships within DApp projects and completing missing public libraries. Using this tool, we created DAPPSCAN-BYTECODE dataset, which consists of 6,665 compiled smart contract with 888 SWC weaknesses. Based on DAPPSCAN-BYTECODE, we conducted an empirical study to evaluate the performance of state-of-the-art smart contract weakness detection tools. The evaluation results revealed sub-par performance for these tools in terms of both effectiveness and success detection rate, indicating that future development should prioritize real-world datasets over simplistic toy contracts.Comment: Dataset available at https://github.com/InPlusLab/DAppSCA

Turn the Rudder: A Beacon of Reentrancy Detection for Smart Contracts on Ethereum

Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guidelines for their application. In this work, we conduct a large-scale empirical study on the capability of five well-known or recent reentrancy detection tools such as Mythril and Sailfish. We collect 230,548 verified smart contracts from Etherscan and use detection tools to analyze 139,424 contracts after deduplication, which results in 21,212 contracts with reentrancy issues. Then, we manually examine the defective functions located by the tools in the contracts. From the examination results, we obtain 34 true positive contracts with reentrancy and 21,178 false positive contracts without reentrancy. We also analyze the causes of the true and false positives. Finally, we evaluate the tools based on the two kinds of contracts. The results show that more than 99.8% of the reentrant contracts detected by the tools are false positives with eight types of causes, and the tools can only detect the reentrancy issues caused by call.value(), 58.8% of which can be revealed by the Ethereum's official IDE, Remix. Furthermore, we collect real-world reentrancy attacks reported in the past two years and find that the tools fail to find any issues in the corresponding contracts. Based on the findings, existing works on reentrancy detection appear to have very limited capability, and researchers should turn the rudder to discover and detect new reentrancy patterns except those related to call.value().Comment: Accepted by ICSE 2023. Dataset available at https://github.com/InPlusLab/ReentrancyStudy-Dat

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

As an energy-intensive industry, the steel industry grapples with increasing energy costs and decarbonisation pressures. Therefore, multi-objective optimisation is widely applied in the production scheduling of the steelmaking plant. However, the optimal solution prioritising energy savings and emission reductions may lead to impractical or less economically efficient solutions, since the processing time requirement (PTR) of steel production orders in real-world production is neglected. This study fills the research gap by discussing the impact of PTR on the make-span of the steelmaking process and incorporating it into the optimisation model. Considering the variability of PTR, the solving of the multi-objective scheduling problem is transformed into the selection from Pareto solutions with different make-spans. To better leverage the temporal flexibility of the steelmaking process, a what-if-analysis-based strategy coupled with the Normal Boundary Intersection method is proposed to generate a series of evenly distributed Pareto solutions. The energy storage system is integrated to improve the time granularity of the steelmaking plant's flexibility. Our case studies demonstrate that the electricity and emission costs are reduced by 68.5%, indirect emissions are reduced by 83.5%, and the on-site renewable energy self-consumption rate increases by 12.1%. The effectiveness of the proposed method implies that it is of great relevance to the development of a cleaner steel industry in the future

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require manually crafted seed alignments, which are expensive to obtain. In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments. Specifically, for predicate embeddings, AutoAlign constructs a predicate-proximity-graph with the help of large language models to automatically capture the similarity between predicates across two KGs. For entity embeddings, AutoAlign first computes the entity embeddings of each KG independently using TransE, and then shifts the two KGs' entity embeddings into the same vector space by computing the similarity between entities based on their attributes. Thus, both predicate alignment and entity alignment can be done without manually crafted seed alignments. AutoAlign is not only fully automatic, but also highly effective. Experiments using real-world KGs show that AutoAlign improves the performance of entity alignment significantly compared to state-of-the-art methods.Comment: 14 pages, 5 figures, 4 tables. arXiv admin note: substantial text overlap with arXiv:2210.0854

Adeno-associated virus serotype rh.10 displays strong muscle tropism following intraperitoneal delivery

Recombinant adeno-associated virus (rAAV) is an attractive tool for basic science and translational medicine including gene therapy, due to the versatility in its cell and organ transduction. Previous work indicates that rAAV transduction patterns are highly dependent on route of administration. Based on this relationship, we hypothesized that intraperitoneal (IP) administration of rAAV produces unique patterns of tissue tropism. To test this hypothesis, we investigated the transduction efficiency of 12 rAAV serotypes carrying an enhanced green fluorescent protein (EGFP) reporter gene in a panel of 12 organs after IP injection. Our data suggest that IP administration emphasizes transduction patterns that are different from previously reported intravascular delivery methods. Using this approach, rAAV efficiently transduces the liver, pancreas, skeletal muscle, heart and diaphragm without causing significant histopathological changes. Of note, rAAVrh.10 showed excellent muscle transduction following IP administration, highlighting its potential as a new muscle-targeting vector